Patient-Specific Heartbeat Classification Based on I-Vector Adapted Deep Neural Networks

Sean Shensheng Xu, Man Wai Mak, Chi Chung Cheung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Automatic heartbeat classification from electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A main challenge in ECG classification is the variability of ECG signals across patients. This paper proposes a patient-specific heartbeat classifier to address the inter-patient variations in ECG signals. Inspired by the success of identity vectors (i-vectors) in speech and speaker recognition, we extracted one i-vector from five minutes of ECG data for each patient and applied it to adapt a patient-independent deep neural network (DNN) to a patient-specific DNN, namely i-vector adapted patient-specific DNN (iAP-DNN). Evaluations on the MIT-BIH arrhythmia database show that the iAP-DNN is able to classify raw ECG signals of the corresponding patient into normal heartbeats and different types of arrhythmias and that it outperforms existing patient-specific classifiers in terms of sensitivity-vs-specificity and Mathews correlation coefficients.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages784-787
Number of pages4
ISBN (Electronic)9781538654880
DOIs
Publication statusPublished - 3 Dec 2018
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period3/12/186/12/18

Keywords

  • Arrhythmias
  • Deep neural networks
  • DNN adaptation
  • ECG classification
  • i-vectors

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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